Currently tracking 489 active AI roles, up 170% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $98k–$505k (avg $233k).
| Title | Stage | AI score |
|---|---|---|
| Research Scientist, Applied ML, Quantum Error Correction Research Scientist role focused on applying machine learning to discover novel Quantum Error Correction (QEC) codes for fault-tolerant quantum computing, specifically for superconducting qubits and high-connectivity platforms. The role involves developing large-scale automated discovery pipelines, optimizing codes for quantum processors, and contributing to the research community through publications and collaborations. | Data | 10 |
| Research Scientist, Visual Data and Generative Research Research Scientist focused on visual data and generative models, specifically for creating high-quality synthetic training data for foundation models. This involves designing data acquisition strategies, optimizing hardware, implementing fine-tuning methods, developing automated labeling pipelines, and creating evaluation datasets for visual quality issues. | Data |
| 9 |
| Senior Staff Software Engineer, Machine Learning, ML Training Senior Staff Software Engineer focused on building and delivering ML frameworks for training large language models (LLMs) and stable diffusion models for Google Cloud customers. The role involves designing and implementing AI frameworks software for various ML workloads, identifying and resolving software and performance issues, and collaborating with cross-functional teams. Requires extensive experience in software development, ML design, ML infrastructure, and leading technical projects, with a focus on training ML models at scale. | DataServe | 9 |
| Senior Staff Data Scientist Manager, AI Data This role focuses on improving the quality of data used for training Machine Learning models, particularly Large Language Models (LLMs). The responsibilities include analyzing large datasets, defining data quality metrics, researching methods to enhance data quality, and influencing product direction through data insights. The role requires significant experience in data analysis and a background in quantitative fields. | Data | 8 |
| Software Engineer, AI/ML Data and Training Infrastructure Software Engineer role focused on building and advancing ML data and training infrastructure to enable ML use cases for recommendation systems. Requires experience in software development, ML models, and ML infrastructure, including data processing, model optimization, evaluation, and deployment. | Data | 7 |
| Staff Software Engineer, ML Data Infrastructure Google's YouTube Discovery Data team is seeking a Staff Software Engineer to build and maintain large-scale data processing pipelines that power personalized discovery and ML models at YouTube. The role involves enabling next-generation model architectures and training procedures, reducing complexity in ML training infrastructure, and collaborating with other infrastructure teams. The ideal candidate will have extensive experience in C++ programming, large-scale infrastructure development, and a solid understanding of ML concepts. | Data | 7 |
| Engineering Manager, Woodshed Engineering Manager for Woodshed, a core AI/ML infrastructure team at Google, focusing on distributed systems for machine learning, dataset lakehouse management (Canon), and supporting multimodal model training for various Google product areas like Gemini and GenMedia. The role involves managing a team, contributing to product strategy, and building next-generation AI/ML systems. | DataPretrain | 7 |
| Image Processing Engineer Image Processing Engineer role focused on optimizing image quality by fine-tuning algorithms and building ML-powered tools for tuning, testing, and calibration workflows. Requires experience in image quality, computer vision, Python, and C++. | Data | 7 |
| Software Engineer III, AI/ML, Health and Home Software Engineer III, AI/ML role focused on developing LLM-based tools for generating synthetic user data and conversations for health and home feature evaluations. This involves implementing ML solutions, utilizing ML infrastructure, and contributing to model optimization and data processing, with a focus on advancing the quality and realism of synthetic context and defining evaluation metrics. | DataAgent | 7 |